Azure Databricks Tech Lead

Avanade is the world’s leading Microsoft expert, helping more than 7,000 organizations modernize securely and scale AI using Microsoft technology, faster. Founded by Microsoft and Accenture, and operating as Microsoft’s Client Zero, Avanade brings deep expertise and proven delivery to simplify complexity, accelerate innovation, and deliver measurable results.

Together with Accenture, Avanade combines global scale with expertise in AI, cloud, data, cybersecurity, and ERP to design solutions that put people first. With 60,000 Microsoft professionals worldwide, 165,000 certifications, and the largest community of Microsoft MVPs, Avanade has spent more than 25 years helping organizations unlock the full potential of people and technology to create lasting impact for clients, employees, and communities. 

Role Summary 

Avanade India’s Data & AI practice is looking for an Azure Databricks Tech Lead to drive the technical delivery of data estate, data lake, data warehouse, and data migration projects and programs built on Azure Databricks. This is a hands-on technical leadership role focused on execution — leading engineering teams through design, build, and deployment, ensuring engineering quality and delivery timelines, and acting as the go-to technical authority on live projects within the practice. 

Key Responsibilities 

• Own technical delivery of Azure Databricks engagements — from solution design through build, testing, deployment, and go-live — across one or more concurrent projects. 

• Lead and mentor a team of data engineers, guiding design decisions, code quality, and best practices on Databricks-based builds. 

• Translate solution architecture into detailed technical design and implementation plans, working closely with Architects and Project/Program Managers. 

• Build and review Azure Databricks pipelines and components: Lakehouse (Delta Lake), Unity Catalog, Databricks SQL, Workflows, and integrations with the broader Azure data ecosystem. 

• Drive engineering standards — coding practices, CI/CD, testing, and performance optimization — across the delivery team. 

• Troubleshoot and resolve complex technical issues during build and post-go-live stabilization; own production support escalations where needed. 

• Track and report delivery progress, risks, and technical dependencies to project/program leadership. 

• Support estimation and technical inputs for ongoing and upcoming project phases within the practice. 

• Contribute to reusable delivery accelerators, patterns, and best practices for the Data & AI practice. 

.

Required Skills & Experience 

• 10–12 years in data engineering/technical delivery roles, with strong recent hands-on experience delivering Azure Databricks-based solutions. 

• Deep hands-on expertise with Databricks: Lakehouse/Delta Lake, Unity Catalog, Databricks SQL, Workflows, and cluster/compute optimization. 

• Strong grounding in modern data estate patterns — data lake and data warehouse architecture (medallion architecture, dimensional modeling). 

• Experience leading or being a key technical contributor on data migration projects — legacy on-prem or cloud platforms to Azure Databricks. 

• Solid experience across the broader Azure data ecosystem: Azure Data Factory, Azure Synapse Analytics, ADLS Gen2, Azure SQL. 

• Strong SQL, data modeling, and performance tuning skills; strong hands-on Spark/PySpark expertise. 

• Working knowledge of data governance and security practices — Unity Catalog, role-based access, data lineage. 

• Proven experience leading a delivery team — technical mentoring, code/design reviews, and hands-on problem-solving under project timelines. 

• Comfortable working directly with project/program managers and client technical teams during delivery. 

Preferred / Good to Have 

• At least 3 years of hands-on experience specifically on Azure Databricks. 

• Databricks certifications: Databricks Certified Data Engineer Professional or Databricks Certified Associate Developer for Apache Spark. 

• Exposure to CI/CD and DevOps practices for data platforms (Azure DevOps, GitHub Actions). 

• Exposure to AI/ML integration on Databricks (MLflow, Databricks Model Serving) or broader GenAI/agentic data use cases. 

• Experience working across multiple industry domains (manufacturing, BFSI, consumer goods, energy, healthcare, or others). 

• Experience working in a global delivery / SI environment with distributed teams across geographies. 

.